Dodsonmorrison3497

Abnormal autophagy is related to the pathogenesis and clinical symptoms of myelodysplastic syndrome (MDS). However, the effect of autophagy-related genes (ARGs) on the prognosis of MDS remains unclear. Here, we examined the expression profile of 108 patients with MDS from the GSE58831 dataset, and identified 22 genes that were significantly associated with overall survival. Among them, seven ARGs were screened and APIs were calculated for all samples based on the expression of the seven ARGs, and then, MDS patients were categorized into high- and low-risk groups based on the median APIs. The overall survival of patients with high-risk scores based on these seven ARGs was shorter than patients with low-risk scores in both the training cohort (P = 2.851e-06) and the validation cohort (P = 9.265e-03). Additionally, API showed an independent prognostic indicator for survival in the training samples [hazard ratio (HR) = 1.322, 95% confidence interval (CI) 1.158-1.51; P less then 0.001] and the validation cohort (HR = 1.05, 95% CI 1-1.1; P less then 0.01). The area under the receiver operating characteristic curve (AUROC) of API and IPSS were 43.0137 and 66.0274 in the training cohorts and the AUC of the validation cohorts were 41.5361 and 72.0219. Our data indicate these seven ARGs can predict prognosis in patients with MDS and could guide individualized treatment.Due to the high resistance that cancer has shown to conventional therapies, it is difficult to treat this disease, particularly in advanced stages. In recent decades, treatments have been improved, being more specific according to the characteristics of the tumor, becoming more effective, less toxic, and invasive. Cancer can be treated by the combination of surgery, radiation therapy, and/or drug administration, but therapies based on anticancer drugs are the main cancer treatment. Cancer drug development requires long-time preclinical and clinical studies and is not cost-effective. Drug repurposing is an alternative for cancer therapies development since it is faster, safer, easier, cheaper, and repurposed drugs do not have serious side effects. However, cancer is a complex, heterogeneous, and highly dynamic disease with multiple evolving molecular constituents. This tumor heterogeneity causes several resistance mechanisms in cancer therapies, mainly the target mutation. The CRISPR-dCas9-based artificial transcription factors (ATFs) could be used in cancer therapy due to their possibility to manipulate DNA to modify target genes, activate tumor suppressor genes, silence oncogenes, and tumor resistance mechanisms for targeted therapy. In addition, drug repurposing combined with the use of CRISPR-dCas9-based ATFs could be an alternative cancer treatment to reduce cancer mortality. The aim of this review is to describe the potential of the repurposed drugs combined with CRISPR-dCas9-based ATFs to improve the efficacy of cancer treatment, discussing the possible advantages and disadvantages.

Dysregulated oncomiRs are attributed to hepatocellular carcinoma (HCC) through targeting mTOR signaling pathway responsible for cell growth and proliferation. The potential of these oncomiRs as biomarker for tumor response or as target for therapy needs to be evaluated.

Tumor response assessment by OncomiR changes following locoregional therapy (LRT) and targeting of these oncomiRs modulating pathway.

All consecutive viral-HCC patients of BCLC stage-A/B undergoing LRT were included. OncomiRs (miR-21, -221, and -16) change in circulation and AFP-ratio at 1-month post-LRT to baseline was estimated to differentiate various categories of response as per mRECIST criteria. OncomiR modulating mTOR pathway was studied by generating miR-21 and miR-221 overexpressing Huh7 stable cell lines.

Post-LRT tumor response was assessed in 90 viral-HCC patients (CR, 40%; PR, 31%, and PD, 29%). Significant increase of miRNA-21 and -221 expression was observed in PD (p = 0.040, 0.047) and PR patients (miR-21, p = 0.045). click here Fold changes of miR-21 can differentiate response ingroup (CR from PR+PD) at AUROC 0.718 (95% CI, 0.572-0.799) and CR from PD at AUROC 0.734 (95% CI, 0.595-0.873). Overexpression of miR-21 in hepatoma cell line had shown increased phosphorylation p70S6K, the downstream regulator of cell proliferation in mTOR pathway. Upregulation of AKT, mTOR, and RPS6KB1 genes were found significant (P < 0.005) and anti-miR-21 specifically reduced mTOR gene (P = 0.02) expression.

The miR-21 fold change correlates well with imaging in predicting tumor response. Overexpression of miR-21 has a role in HCC through mTOR pathway activation and can be targeted.

The miR-21 fold change correlates well with imaging in predicting tumor response. Overexpression of miR-21 has a role in HCC through mTOR pathway activation and can be targeted.

To evaluate a combination of texture features and machine learning-based analysis of apparent diffusion coefficient (ADC) maps for the prediction of Grade Group (GG) upgrading in Gleason score (GS) ≤6 prostate cancer (PCa) (GG1) and GS 3 + 4 PCa (GG2).

Fifty-nine patients who were biopsy-proven to have GG1 or GG2 and underwent MRI examination with the same MRI scanner prior to transrectal ultrasound (TRUS)-guided systemic biopsy were included. All these patients received radical prostatectomy to confirm the final GG. Patients were divided into training cohort and test cohort. 94 texture features were extracted from ADC maps for each patient. The independent sample t-test or Mann-Whitney U test was used to identify the texture features with statistically significant differences between GG upgrading group and GG non-upgrading group. Texture features of GG1 and GG2 were compared based on the final pathology of radical prostatectomy. We used the least absolute shrinkage and selection operator (LASSO) algorithhad no significant difference between AUCs in the test cohort.

A combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.

A combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.